However, the idea of “learn to learn” is attractive. MAML[1]} is the optimization based meta-learning work that inspired OOC-ML proposed as a major component of portal learning. The differences are major. OOC-ML algorithm expands on it by focusing on data feature distribution instead of label distribution, encouraging active sampling in local neighborhood, which simplifies the support/query meta-train/meta-test data logistics, and ensembling a few local loss directions to learn global gravity direction.
